Hybrid IIoT intrusion detection enhancement with A3C

Traditional Intrusion Detection Systems often face difficulties in detecting the diverse classes of intrusions in the Industrial Internet of Things (IIoT) environment due to its heterogeneity and complexity. Such environments encounter a mixture of deterministic and stochastic cyberattack patterns, corresponding to discrete and continuous features. This challenge results in inefficient feature extraction in current Asynchronous Advantage Actor-Critic (A3C) algorithms. To address these issues, this article proposes a Hybrid A3C with Decision Tree (HADT) algorithm for intrusion detection. In this proposed algorithm, the simple A3C is used to detect stochastic intrusions related to continuous features, while the Decision Tree (DT) algorithm is employed for detecting fixed-pattern (deterministic) intrusions related to discrete features. The outputs of these algorithms are combined in a Feedforward Neural Network (FFNN) layer to accurately classify the intrusion type. Additionally, the X-IIoTID dataset, obtained from a real IIoT environment, was used to compare the performance of HADT with existing A3C algorithms in related studies. Experimental results demonstrate that the HADT algorithm outperforms baseline A3C algorithms in terms of accuracy, precision, recall, and F1 score, maintaining a detection rate above 99.8%, especially for intrusions that are underrepresented in the dataset, while also consuming less power.